Any nucleus that possesses a magnetic moment attempts to align itself with the direction of the magnetic field in which it is located. In doing so, however, the nucleus precesses around this direction at a characteristic angular frequency (Larmor frequency), which is dependent on the strength of the magnetic field and on the properties of the specific nuclear species (the magnetogyric constant γ of the nucleus). Nuclei which exhibit these phenomena are referred to herein as “spins.”
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. A net magnetic moment MZ is produced in the direction of the polarizing field, but the randomly oriented magnetic components in the perpendicular, or transverse, plane (x-y plane) cancel one another. If, however, the substance, or tissue, is subjected to a transient electromagnetic pulse (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt, which is rotating, or spinning, in the x-y plane at the Larmor frequency. The practical value of this phenomenon resides on signals that are emitted by the excited spins after the pulsed excitation signal B1 is terminated. Depending upon chemically and biologically determined variable parameters such as proton density, longitudinal relaxation time (“T1”) describing the recovery of MZ along the polarizing field, and transverse relaxation time (“T2”) describing the decay of Mt in the x-y plane, this nuclear magnetic resonance (“NMR”) phenomena is exploited to obtain image contrast and concentrations of chemical entities or metabolites using different measurement sequences and by changing imaging parameters.
When utilizing NMR to produce images and chemical spectra, a technique is employed to obtain NMR signals from specific locations in the subject. Typically, the region to be imaged (region of interest) is scanned using a sequence of NMR measurement cycles that vary according to the particular localization method being used. To perform such a scan, it is, of course, necessary to elicit NMR signals from specific locations in the subject. This is accomplished by employing magnetic fields (Gx, Gy, and Gz) which have the same direction as the polarizing field B0, but which have a gradient along the respective x, y and z axes. By controlling the strength of these gradients during each NMR cycle, the spatial distribution of spin excitation can be controlled and the location of the resulting NMR signals can be identified from the Larmor frequencies typical of the local field. The acquisition of the NMR signals is referred to as sampling k-space, and a scan is completed when sufficient NMR cycles are performed to fully or partially sample k-space. The resulting set of received NMR signals are digitized and processed to reconstruct the image using various reconstruction techniques.
To generate an MR anatomic image, gradient pulses are typically applied along the x, y and z-axis directions to localize the spins along the three spatial dimensions, and MR signals are acquired in the presence of one or more readout gradient pulses. An image depicting the spatial distribution of a particular nucleus in a region of interest of the object is then generated, using known post-processing techniques. Typically, the hydrogen nucleus (1H) is imaged, though other MR-detectable nuclei may also be used to generate images and chemical spectra.
Similar to structural MRI, magnetic resonance spectroscopy (“MRS”) utilizes a magnetic field and radio frequency transmit pulses to observe signals from specific nuclei in specific molecules. Example MRS nuclei include hydrogen (1H) or protons, as well as carbon-13 (13C), fluorine-19 (19F), phosphorous-31 (31P) and others. However, in contrast to structural MRI imaging, MR signals in MRS are typically acquired in the absence of readout gradient pulses. In addition, acquired MR signals are used to generate spectra indicating the chemical species present in selected voxels or volumes of interest (“VOIs”). The spectra can include several spectral peaks whose amplitudes (area) are associated with the concentrations of the various chemical species. Specifically, the peak positions represent the different resonance frequencies, or chemical shift, experienced by the detected nuclei due to different chemical (molecular) environments. Typically, the spectra are displayed in units of parts per million (“ppm”) separation from a standard peak, since resonant frequency differences are typically on the order of a few Hz and MR signals generally precess in the MHz frequency range. For example, the chemical shift between protons in fat and water has been measured to be approximately 3.5 ppm, or approximately 220 Hz at 1.5 Tesla.
Oftentimes MRS studies involve comparing spectra obtained from different subjects, or populations of subjects, or tracking spectra for a given subject as a function of time. In doing so, it is important to be consistent in the selection of voxels or VOIs in order to generate accurate results and draw meaningful conclusions. Presently, voxel selection in MRS is performed manually, which is prone to appreciable error due to inter- and intra-operator variability. In addition, anatomic differences between different subjects makes selecting voxels manually and in a consistent manner difficult.
Automated voxel or VOI selection offers the promise of improved consistency. Existing technologies, such as the AutoAlign tool on Siemens, align scout images of a subject to predefined landmarks using rigid registration algorithms to ensure that subsequent images are acquired in a common space. As such, more reliable VOI positioning can be achieved on follow-up scans of the same subject. However, this approach does not guarantee that any particular brain structure is consistently aligned across different subjects with anatomical variability. To ensure consistency between subjects and prescribe VOIs individualized to subjects, one automated method utilizes pre-segmented brain regions (defined on a template) and an algorithm that calculates the tightest fitting oriented bounding box. However, this approach requires several hours of off-line processing as well as structures that can be clearly segmented. As such, this technique would not be applicable to brain structures that may not have clearly discernible borders, such as white matter brain regions.
Therefore, given the above, there is a need for improved systems and methods for voxel or VOI selection in MRS.